NVIDIA NIM on GKE

Type: Deployment Guide Tags: NVIDIA, NIM, Google Kubernetes Engine, GKE, Kubernetes, Google Cloud, inference, cloud deployment Related: NVIDIA-NIM, NIM-for-Large-Language-Models, NIM-for-LLM-Benchmarking-Guide, NVIDIA-NIM-Operator, NVIDIA-GPU-Operator, NVIDIA-Container-Toolkit, NVIDIA-AI-Enterprise-Cloud-Deployment, NVIDIA-Cloud-Native-Technologies, NGC Sources: https://docs.nvidia.com/nim/cloud/gke/latest/index.html, https://docs.nvidia.com/nim/cloud/gke/latest/overview.html, https://docs.nvidia.com/nim/cloud/gke/latest/hardware.html Last Updated: 2026-04-29

Summary

NVIDIA NIM on GKE is NVIDIA’s guide for deploying NIM microservices on Google Kubernetes Engine. Current docs offer two deployment paths: an integrated NIM on GKE Kubernetes application from Google Cloud Marketplace for a quick running example, and a Terraform/Helm path for teams that want full control over their cluster and NIM deployment.

Detail

Purpose

NIM on GKE gives Google Cloud users a managed Kubernetes path for self-hosting NIM endpoints while keeping OpenAI-style model APIs. It is useful when teams want cloud-native scaling, Google Cloud operations, and NVIDIA model-serving containers in one deployment pattern.

Current scope

• Integrated NIM on GKE Kubernetes application for quick deployment through Google Cloud Marketplace.
• Terraform and Helm workflow through NVIDIA’s nim-deploy examples for custom clusters.
• Prerequisites include a GCP account, billing, project ownership or a sufficiently privileged service account, and multiple Google Cloud IAM roles.
• Deployment flow selects deployment name, service account, cluster/GPU location, NIM model name, and terms acceptance.
• Deployment typically takes about 15-20 minutes depending on model and cluster parameters.
• Testing uses gcloud cluster credentials, kubectl port forwarding, /v1/health/ready, /v1/models, /v1/chat/completions, /v1/ranking, and /v1/embeddings endpoint examples.
• Optional load testing and performance measurement are documented through NVIDIA’s generative AI performance tooling.
• Hardware support docs list GKE configurations for H100, A100, and L4-backed model profiles.

NVIDIA context

NIM on GKE is one cloud-specific deployment guide within the broader NVIDIA-NIM operations surface. It complements NVIDIA-NIM-Operator and NVIDIA-GPU-Operator for Kubernetes lifecycle management, while NVIDIA-AI-Enterprise-Cloud-Deployment covers the broader AI Enterprise cloud deployment context across providers.

Connections

• NVIDIA-NIM - NIM on GKE is a cloud-specific path for running NIM microservices.
• NIM-for-Large-Language-Models - LLM NIMs are common targets for the GKE deployment path.
• NIM-for-LLM-Benchmarking-Guide - GKE docs point to performance testing after deployment.
• NVIDIA-NIM-Operator - Kubernetes lifecycle manager adjacent to production NIM deployments.
• NVIDIA-GPU-Operator - underlying Kubernetes GPU enablement layer.
• NVIDIA-Container-Toolkit - runtime layer for GPU-enabled containers in Kubernetes nodes.
• NVIDIA-AI-Enterprise-Cloud-Deployment - broader cloud deployment guide for AI Enterprise and managed Kubernetes paths.
• NVIDIA-Cloud-Native-Technologies - NVIDIA cloud-native hub for Kubernetes, operators, and container deployment.
• NGC - NIM containers and artifacts are distributed through NVIDIA catalog/registry workflows.

Source Excerpts

• NVIDIA docs describe two NIM on GKE paths: the integrated Kubernetes application and a Terraform/Helm custom-cluster path.
• Current hardware docs list optimized model profiles across H100, A100, and L4 GKE configurations.

Resources

• NIM on GKE Overview
• NIM on GKE Hardware Support